{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBRw5QHhBkax"
      },
      "source": [
        "# 数据集导入(预制链接)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# !rm -rf unziped"
      ],
      "metadata": {
        "id": "8WVPhaPN9pjO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 官方数据集下载(若要验证我们下载的数据集来源请打开注释)"
      ],
      "metadata": {
        "id": "TiWCrjj_u22c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "UE_6BNHmHIlH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "04d4723a-6200-4e25-a52f-41ce820c84a0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-16 18:08:51--  https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=8ce890e0-0019-4e1e-ac63-14718948f612&at=APZUnTW-e7sn7C7k5UVU2BaxZPGT%3A1721020888524\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.250.4.132, 2404:6800:4003:c06::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.250.4.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1084182095 (1.0G) [application/octet-stream]\n",
            "Saving to: ‘dataset_1.zip’\n",
            "\n",
            "dataset_1.zip       100%[===================>]   1.01G  42.0MB/s    in 34s     \n",
            "\n",
            "2024-07-16 18:09:28 (30.7 MB/s) - ‘dataset_1.zip’ saved [1084182095/1084182095]\n",
            "\n",
            "--2024-07-16 18:09:28--  https://drive.usercontent.google.com/download?id=1izP72pHtoXpQvOV8WFCnh_LekzLunyG5&export=download&authuser=0&confirm=t&uuid=8e453e3d-84ac-4f51-9cbf-45d47cbdcc65&at=APZUnTVfJYZBQwnHawB72aq5MPvv%3A1721020973099\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.250.4.132, 2404:6800:4003:c06::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.250.4.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4740031429 (4.4G) [application/octet-stream]\n",
            "Saving to: ‘dataset_2.zip’\n",
            "\n",
            "dataset_2.zip       100%[===================>]   4.41G  58.0MB/s    in 1m 41s  \n",
            "\n",
            "2024-07-16 18:11:11 (44.8 MB/s) - ‘dataset_2.zip’ saved [4740031429/4740031429]\n",
            "\n",
            "--2024-07-16 18:11:11--  https://drive.usercontent.google.com/download?id=1djT0tlmLBi15LYZG0dxci1RSjPI94sM8&export=download&authuser=0&confirm=t&uuid=4687dd5d-a001-47f2-bacd-e72d5c7361e4&at=APZUnTWWEM2OCtpaZNuS4UQjMzxc%3A1721021154071\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.250.4.132, 2404:6800:4003:c06::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.250.4.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4217480891 (3.9G) [application/octet-stream]\n",
            "Saving to: ‘dataset_3.zip’\n",
            "\n",
            "dataset_3.zip       100%[===================>]   3.93G  34.1MB/s    in 1m 42s  \n",
            "\n",
            "2024-07-16 18:12:55 (39.5 MB/s) - ‘dataset_3.zip’ saved [4217480891/4217480891]\n",
            "\n",
            "Archive:  dataset_1.zip\n",
            "   creating: unziped/dataset_1/Dataset/\n",
            "   creating: unziped/dataset_1/Dataset/Testset_track_A/\n",
            "   creating: unziped/dataset_1/Dataset/Testset_track_A/Inference/\n",
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            "   creating: unziped/dataset_1/Dataset/Testset_track_B/\n",
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            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0197.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0361.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0038.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0471.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0333.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0324.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0330.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0740.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0275.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0371.ply  \n",
            "  inflating: unziped/dataset_3/train_mesh_0603/mesh_0365.ply  \n"
          ]
        }
      ],
      "source": [
        "\n",
        "# !wget --no-check-certificate 'https://drive.usercontent.google.com/download?id=1JwR0Q1ArTg6c47EF2ZuIBpQwCPgXKrO2&export=download&authuser=0&confirm=t&uuid=8ce890e0-0019-4e1e-ac63-14718948f612&at=APZUnTW-e7sn7C7k5UVU2BaxZPGT%3A1721020888524' -O dataset_1.zip\n",
        "# !wget --no-check-certificate 'https://drive.usercontent.google.com/download?id=1izP72pHtoXpQvOV8WFCnh_LekzLunyG5&export=download&authuser=0&confirm=t&uuid=8e453e3d-84ac-4f51-9cbf-45d47cbdcc65&at=APZUnTVfJYZBQwnHawB72aq5MPvv%3A1721020973099' -O dataset_2.zip\n",
        "# !wget --no-check-certificate 'https://drive.usercontent.google.com/download?id=1djT0tlmLBi15LYZG0dxci1RSjPI94sM8&export=download&authuser=0&confirm=t&uuid=4687dd5d-a001-47f2-bacd-e72d5c7361e4&at=APZUnTWWEM2OCtpaZNuS4UQjMzxc%3A1721021154071' -O dataset_3.zip\n",
        "\n",
        "# # Unzip dataset file\n",
        "# import os\n",
        "# import shutil\n",
        "\n",
        "# # 指定新文件夹的名称\n",
        "# new_folder_A = 'unziped/dataset_1'\n",
        "# new_folder_B = 'unziped/dataset_2'\n",
        "# new_folder_C = 'unziped/dataset_3'\n",
        "\n",
        "# # 在当前目录下创建新文件夹\n",
        "# if not os.path.exists(new_folder_A):\n",
        "#     os.makedirs(new_folder_A)\n",
        "# if not os.path.exists(new_folder_B):\n",
        "#     os.makedirs(new_folder_B)\n",
        "# if not os.path.exists(new_folder_B):\n",
        "#     os.makedirs(new_folder_B)\n",
        "\n",
        "# # 使用!unzip命令将文件解压到新文件夹中\n",
        "# # !unzip trackCtrain.zip -d {new_folder}\n",
        "# !unzip dataset_1.zip -d {new_folder_A}\n",
        "# !unzip dataset_2.zip -d {new_folder_B}\n",
        "# !unzip dataset_3.zip -d {new_folder_C}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kY81z-fCgPfK"
      },
      "source": [
        "# 源代码"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!wget --no-check-certificate 'https://drive.usercontent.google.com/download?id=1F9n6o0Hf2H2Jwe14hSVXaWqpSqtHNUMj&export=download&authuser=0' -O trackCsrc.zip\n",
        "!unzip trackCsrc.zip\n"
      ],
      "metadata": {
        "id": "iYjlXfhH39nn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1ef91c1c-e7d2-4276-dc0c-f5919b016b0e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-16 18:16:08--  https://drive.usercontent.google.com/download?id=1F9n6o0Hf2H2Jwe14hSVXaWqpSqtHNUMj&export=download&authuser=0\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.250.4.132, 2404:6800:4003:c06::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.250.4.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 139506 (136K) [application/octet-stream]\n",
            "Saving to: ‘trackCsrc.zip’\n",
            "\n",
            "trackCsrc.zip       100%[===================>] 136.24K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2024-07-16 18:16:10 (94.0 MB/s) - ‘trackCsrc.zip’ saved [139506/139506]\n",
            "\n",
            "Archive:  trackCsrc.zip\n",
            "   creating: dataset/\n",
            " extracting: dataset/__init__.py     \n",
            "   creating: dataset/__pycache__/\n",
            "  inflating: dataset/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: dataset/__pycache__/Load_mesh.cpython-310.pyc  \n",
            "  inflating: dataset/Load_mesh.py    \n",
            "   creating: Extract_mesh/\n",
            " extracting: Extract_mesh/__init__.py  \n",
            "  inflating: Extract_mesh/merge_h5.py  \n",
            "  inflating: Extract_mesh/parse_dataset.py  \n",
            "   creating: NN/\n",
            "  inflating: NN/__init__.py          \n",
            "   creating: NN/__pycache__/\n",
            "  inflating: NN/__pycache__/__init__.cpython-310.pyc  \n",
            "   creating: NN/GNN/\n",
            "   creating: NN/GNN/FiniteVolumeGN/\n",
            "   creating: NN/GNN/FiniteVolumeGN/__pycache__/\n",
            "  inflating: NN/GNN/FiniteVolumeGN/__pycache__/blocks.cpython-310.pyc  \n",
            "  inflating: NN/GNN/FiniteVolumeGN/__pycache__/EPDbackbone.cpython-310.pyc  \n",
            "  inflating: NN/GNN/FiniteVolumeGN/blocks.py  \n",
            "  inflating: NN/GNN/FiniteVolumeGN/EPDbackbone.py  \n",
            "  inflating: NN/GNN/FiniteVolumeGN/FVGN.py  \n",
            "   creating: NN/Transolver/\n",
            "   creating: NN/Transolver/__pycache__/\n",
            "  inflating: NN/Transolver/__pycache__/FVGNAttUnet.cpython-310.pyc  \n",
            "  inflating: NN/Transolver/__pycache__/Model.cpython-310.pyc  \n",
            "  inflating: NN/Transolver/__pycache__/SageTrans_importer_B.cpython-310.pyc  \n",
            "  inflating: NN/Transolver/FVGNAttUnet.py  \n",
            "  inflating: NN/Transolver/FVGNUnet.py  \n",
            "  inflating: NN/Transolver/Model.py  \n",
            "  inflating: NN/Transolver/SageSDFUnet.py  \n",
            "  inflating: NN/Transolver/SageTrans_importer.py  \n",
            "  inflating: NN/Transolver/SageTrans_importer_B.py  \n",
            "  inflating: NN/Transolver/SDFUnet.py  \n",
            "  inflating: NN/Transolver/TransAttUnet.py  \n",
            "  inflating: NN/Transolver/TransolverUnet.py  \n",
            "  inflating: NN/Transolver/TransU_sep.py  \n",
            "   creating: NN/UNet/\n",
            "   creating: NN/UNet/__pycache__/\n",
            "  inflating: NN/UNet/__pycache__/attention_unet.cpython-310.pyc  \n",
            "  inflating: NN/UNet/__pycache__/BuildingBlocks.cpython-310.pyc  \n",
            "  inflating: NN/UNet/__pycache__/unet_parts.cpython-310.pyc  \n",
            "  inflating: NN/UNet/attention_unet.py  \n",
            "  inflating: NN/UNet/BuildingBlocks.py  \n",
            "  inflating: NN/UNet/Model.py        \n",
            "  inflating: NN/UNet/Unet_GINO.py    \n",
            "  inflating: NN/UNet/unet_model.py   \n",
            "  inflating: NN/UNet/unet_parts.py   \n",
            "  inflating: requirements.txt        \n",
            "   creating: utils/\n",
            " extracting: utils/__init__.py       \n",
            "   creating: utils/__pycache__/\n",
            "  inflating: utils/__pycache__/__init__.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/DS_utils.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/get_param.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/Logger.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/losses.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/normalization.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/scheduler.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/utilities.cpython-310.pyc  \n",
            "  inflating: utils/__pycache__/write_tec.cpython-310.pyc  \n",
            "  inflating: utils/DS_utils.py       \n",
            "  inflating: utils/get_param.py      \n",
            "  inflating: utils/Logger.py         \n",
            "  inflating: utils/losses.py         \n",
            "  inflating: utils/normalization.py  \n",
            "  inflating: utils/scheduler.py      \n",
            "  inflating: utils/utilities.py      \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ITzT8s2wgZG0"
      },
      "source": [
        "# 下载我们自己的数据集"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vFRcy7gOHIlI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "affb77b1-275d-4693-a84c-e63d4f90c8ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-07-16 18:16:17--  https://drive.usercontent.google.com/download?id=1lUV7mJaoWPfRks4DMhJgDCxTZwMr8LQI&export=download&authuser=0&confirm=t&uuid=fb0fecae-3804-4169-ad33-80b7ba7c3d6f&at=APZUnTWlNtpAliQOv6JvKUgX6zR7%3A1721123877599\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.250.4.132, 2404:6800:4003:c06::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.250.4.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 20130032904 (19G) [application/octet-stream]\n",
            "Saving to: ‘trackCtrain.zip’\n",
            "\n",
            "trackCtrain.zip     100%[===================>]  18.75G  64.2MB/s    in 5m 29s  \n",
            "\n",
            "2024-07-16 18:21:48 (58.4 MB/s) - ‘trackCtrain.zip’ saved [20130032904/20130032904]\n",
            "\n",
            "--2024-07-16 18:21:48--  https://drive.usercontent.google.com/download?id=1imPyFjFqAj5WTT_K-lHe7_6lFJ4azJ1L&export=download&authuser=0&confirm=t&uuid=ca9d62d3-394a-4798-b2f3-03455a381cf0&at=APZUnTVrYJw4okYQJSrdDXLPqdSX%3A1721020169107\n",
            "Resolving drive.usercontent.google.com (drive.usercontent.google.com)... 142.250.4.132, 2404:6800:4003:c06::84\n",
            "Connecting to drive.usercontent.google.com (drive.usercontent.google.com)|142.250.4.132|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 1926241183 (1.8G) [application/octet-stream]\n",
            "Saving to: ‘trackCtest.zip’\n",
            "\n",
            "trackCtest.zip      100%[===================>]   1.79G   126MB/s    in 17s     \n",
            "\n",
            "2024-07-16 18:22:07 (111 MB/s) - ‘trackCtest.zip’ saved [1926241183/1926241183]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --no-check-certificate 'https://drive.usercontent.google.com/download?id=1lUV7mJaoWPfRks4DMhJgDCxTZwMr8LQI&export=download&authuser=0&confirm=t&uuid=fb0fecae-3804-4169-ad33-80b7ba7c3d6f&at=APZUnTWlNtpAliQOv6JvKUgX6zR7%3A1721123877599' -O trackCtrain.zip\n",
        "!wget --no-check-certificate 'https://drive.usercontent.google.com/download?id=1imPyFjFqAj5WTT_K-lHe7_6lFJ4azJ1L&export=download&authuser=0&confirm=t&uuid=ca9d62d3-394a-4798-b2f3-03455a381cf0&at=APZUnTVrYJw4okYQJSrdDXLPqdSX%3A1721020169107' -O trackCtest.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6ZoqAa1eHIlJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b6324511-e880-4fcd-d1f1-7fe25b00ec3b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  trackCtrain.zip\n",
            "  inflating: Datasets/train.h5       \n",
            "Archive:  trackCtest.zip\n",
            "  inflating: Datasets/test.h5        \n"
          ]
        }
      ],
      "source": [
        "# Unzip dataset file\n",
        "import os\n",
        "\n",
        "# 指定新文件夹的名称\n",
        "new_folder = 'Datasets'\n",
        "\n",
        "# 在当前目录下创建新文件夹\n",
        "if not os.path.exists(new_folder):\n",
        "    os.makedirs(new_folder)\n",
        "\n",
        "# 使用!unzip命令将文件解压到新文件夹中\n",
        "!unzip trackCtrain.zip -d {new_folder}\n",
        "!unzip trackCtest.zip -d {new_folder}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRLfyacGYGcY"
      },
      "source": [
        "## 依赖安装"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sIgKvOCMHIlJ",
        "collapsed": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3ad41183-bb25-400e-9235-acd7dc25bf15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting torch_geometric\n",
            "  Downloading torch_geometric-2.5.3-py3-none-any.whl (1.1 MB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/1.1 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━\u001b[0m \u001b[32m1.0/1.1 MB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m22.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (4.66.4)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.11.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (2023.6.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.1.4)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.9.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (2.31.0)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (3.1.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (1.2.2)\n",
            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from torch_geometric) (5.9.5)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->torch_geometric) (4.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch_geometric) (2.1.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torch_geometric) (2024.7.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch_geometric) (3.5.0)\n",
            "Installing collected packages: torch_geometric\n",
            "Successfully installed torch_geometric-2.5.3\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.3.0+cu121.html\n",
            "Collecting pyg_lib\n",
            "  Downloading https://data.pyg.org/whl/torch-2.3.0%2Bcu121/pyg_lib-0.4.0%2Bpt23cu121-cp310-cp310-linux_x86_64.whl (2.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.5/2.5 MB\u001b[0m \u001b[31m43.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-2.3.0%2Bcu121/torch_scatter-2.1.2%2Bpt23cu121-cp310-cp310-linux_x86_64.whl (10.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.9/10.9 MB\u001b[0m \u001b[31m97.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torch_sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-2.3.0%2Bcu121/torch_sparse-0.6.18%2Bpt23cu121-cp310-cp310-linux_x86_64.whl (5.1 MB)\n",
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            "\u001b[?25hCollecting torch_cluster\n",
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            "Installing collected packages: dash-table, dash-html-components, dash-core-components, addict, widgetsnbextension, typeguard, trimesh, smmap, setproctitle, sentry-sdk, retrying, pyquaternion, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, jedi, einops, docker-pycreds, configargparse, conda-pack, comm, nvidia-cusparse-cu12, nvidia-cudnn-cu12, gitdb, circle_fit, vtk, nvidia-cusolver-cu12, ipywidgets, GitPython, dash, wandb, pyvista, torchtyping, torch_harmonics, open3d, timm\n",
            "  Attempting uninstall: widgetsnbextension\n",
            "    Found existing installation: widgetsnbextension 3.6.7\n",
            "    Uninstalling widgetsnbextension-3.6.7:\n",
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            "  Attempting uninstall: ipywidgets\n",
            "    Found existing installation: ipywidgets 7.7.1\n",
            "    Uninstalling ipywidgets-7.7.1:\n",
            "      Successfully uninstalled ipywidgets-7.7.1\n",
            "Successfully installed GitPython-3.1.43 addict-2.4.0 circle_fit-0.2.1 comm-0.2.2 conda-pack-0.8.0 configargparse-1.7 dash-2.17.1 dash-core-components-2.0.0 dash-html-components-2.0.0 dash-table-5.0.0 docker-pycreds-0.4.0 einops-0.8.0 gitdb-4.0.11 ipywidgets-8.1.3 jedi-0.19.1 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.82 nvidia-nvtx-cu12-12.1.105 open3d-0.18.0 pyquaternion-0.9.9 pyvista-0.44.0 retrying-1.3.4 sentry-sdk-2.10.0 setproctitle-1.3.3 smmap-5.0.1 timm-1.0.7 torch_harmonics-0.6.5 torchtyping-0.1.4 trimesh-4.4.3 typeguard-4.3.0 vtk-9.3.1 wandb-0.17.4 widgetsnbextension-4.0.11\n"
          ]
        }
      ],
      "source": [
        "!pip install torch_geometric\n",
        "!pip install --no-index pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://pytorch-geometric.com/whl/torch-2.3.0+cu121.html\n",
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 数据集来源验证(已注释, 若要运行请先下载官方数据，打开注释，同时修改路径变量)"
      ],
      "metadata": {
        "id": "WXha1v_3_PcX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# import sys\n",
        "# import os\n",
        "# import torch\n",
        "# from torch.optim import Adam\n",
        "# import h5py\n",
        "# import random\n",
        "# from utils import DS_utils\n",
        "# import numpy as np\n",
        "# import trimesh\n",
        "\n",
        "\n",
        "\n",
        "# train_h5 = \"Datasets/train.h5\"\n",
        "\n",
        "# origin_A_mesh = \"unziped/dataset_1/Dataset/Training_data/Feature\"\n",
        "# origin_A_label = \"unziped/dataset_1/Dataset/Training_data/Label\"\n",
        "\n",
        "# origin_B_data = \"unziped/dataset_2\"\n",
        "# origin_B_mesh = \"unziped/dataset_3/train_mesh_0603\"\n",
        "\n",
        "\n",
        "# attrib_help = {\n",
        "#     \"cells_face\": \"构成单元的边索引\",\n",
        "#     \"cells_index\": \"单元的索引, 与cells_face对应\",\n",
        "#     \"cells_node\": \"构成cell的点索引\",\n",
        "#     \"cell|cells_area\": \"cell的面积\",\n",
        "#     \"cell|centroid\": \"cell的中心点\",\n",
        "#     \"face|face_center_pos\": \"边的中心点\",\n",
        "#     \"face|face_length\": \"边的长度\",\n",
        "#     \"face|face_node\": \"构成边的点索引\",\n",
        "#     \"face|neighbour_cell\": \"与边邻接的cell索引\",\n",
        "#     \"node|ao\": \"预计算属性, ambient occlusion\",\n",
        "#     \"cell|ao\": \"预计算属性, ambient occlusion\",\n",
        "#     \"node|pos\": \"坐标\",\n",
        "#     \"node|pressure\": \"压强\",\n",
        "#     \"node|unit_norm_v\": \"单位法向\",\n",
        "#     \"voxel|grid\": \"SDF场的查询点坐标\",\n",
        "#     \"voxel|sdf\": \"SDF场\",\n",
        "#     \"PN_mean_std\": \"坐标和法向的mean与std\",\n",
        "#     \"bounds\": \"该数据的坐标bound\",\n",
        "#     \"cell|centroid\": \"单元的中心坐标\",\n",
        "#     \"face|face_center_pos\": \"单元中心点的邻接关系\",\n",
        "#     \"cell|pressure\": \"在单元上的压强\",\n",
        "#     \"cell|area\": \"单元的面积\",\n",
        "#     \"cell|unit_norm_v\": \"cell的法向\",\n",
        "#     \"pressure_mean_std\": \"该数据样本压强的mean和std, trackA和trackB分别是各自的一组\",\n",
        "#     \"voxel_mean_std\": \"sdf的mean和std\",\n",
        "# }\n",
        "\n",
        "\n",
        "# def show_data_info(sample_handel):\n",
        "#     sample_attrib_keys = list(sample_handel.keys())\n",
        "\n",
        "#     prt_format = \"{:<20}\\t{:<25}\\t{:<50}\\t{}\"\n",
        "#     print(prt_format.format(\"属性\", \"形状\", \"解释\", \"数据类型\"))\n",
        "#     for attrib_key in sample_attrib_keys:\n",
        "\n",
        "#         if attrib_key == \"key_list\":\n",
        "#             # 这是数据集的索引列表，没太大用，属于设计错误\n",
        "#             # 对于trackA的数据集 key_list为['A_1', 'A_2',.....,'A_500']\n",
        "#             # 对于trackB的数据集 key_list为['B_1', 'B_2',.....,'B_500']\n",
        "#             key_list = sample_handel[attrib_key]\n",
        "#             continue\n",
        "\n",
        "#         if sample_handel[attrib_key] is not None:\n",
        "#             shape = sample_handel[attrib_key][:].shape\n",
        "#             dtype = str(sample_handel[attrib_key][:][0].dtype)\n",
        "#             print(\n",
        "#                 prt_format.format(\n",
        "#                     attrib_key, str(shape), attrib_help[attrib_key], dtype\n",
        "#                 )\n",
        "#             )\n",
        "\n",
        "\n",
        "# # 训练样本属性遍历\n",
        "# with h5py.File(train_h5) as h5_handle:\n",
        "\n",
        "#     samples_indices = list(h5_handle.keys())\n",
        "\n",
        "#     print(\n",
        "#         f\"样本总数: {len(samples_indices)},\",\n",
        "#         \"前50%是trackA的数据集, 后50%是trackB的数据集\",\n",
        "#     )\n",
        "\n",
        "#     rand_idx_A = random.randint(0, len(samples_indices) // 2)\n",
        "#     rand_idx_B = random.randint(len(samples_indices) // 2, len(samples_indices))\n",
        "\n",
        "#     sample_handel_A = h5_handle[samples_indices[rand_idx_A]]\n",
        "#     sample_handel_B = h5_handle[samples_indices[rand_idx_B]]\n",
        "\n",
        "#     print(\">>>>>>>>>>>>>>>>>  Track A数据的样本属性和解释  >>>>>>>>>>>>>>>>>\")\n",
        "#     show_data_info(sample_handel_A)\n",
        "\n",
        "#     print(\"\")\n",
        "#     print(\">>>>>>>>>>>>>>>>>  Track B数据的样本属性和解释  >>>>>>>>>>>>>>>>>\")\n",
        "#     show_data_info(sample_handel_B)\n",
        "\n",
        "\n",
        "# def compare_A_data(data, idx):\n",
        "#     idx = idx.split(\"_\")[-1]\n",
        "#     while len(idx) != 3:  # 对于小于三位数的补齐为001\n",
        "#         idx = \"0\" + idx\n",
        "\n",
        "#     origin_mesh_path = os.path.join(origin_A_mesh, f\"mesh_{idx}.ply\")\n",
        "#     points, cells = DS_utils.load_mesh_ply_vtk(origin_mesh_path)\n",
        "\n",
        "#     pressure_h5 = data[\"node|pressure\"][:]\n",
        "#     pressure_gt = np.load(os.path.join(origin_A_label, f\"press_{idx}.npy\"))\n",
        "#     pressure_gt = np.concatenate((pressure_gt[0:16], pressure_gt[112:]), axis=0)[\n",
        "#         :, None\n",
        "#     ]\n",
        "#     pos_h5 = data[\"node|pos\"][:]\n",
        "#     pos = points\n",
        "\n",
        "#     pos_eq = np.allclose(pos_h5, pos, rtol=1e-05, atol=1e-08)\n",
        "#     label_eq = np.allclose(pressure_h5, pressure_gt, rtol=1e-05, atol=1e-08)\n",
        "\n",
        "#     if not pos_eq or not label_eq:\n",
        "#         print(f\"data {idx} is not eq!\")\n",
        "#         return False\n",
        "#     return True\n",
        "\n",
        "\n",
        "# def compare_B_data(data, idx):\n",
        "#     idx = idx.split(\"_\")[-1]\n",
        "#     while len(idx) != 4:  # 对于小于三位数的补齐为001\n",
        "#         idx = \"0\" + idx\n",
        "\n",
        "#     origin_centroid_path = os.path.join(origin_B_data, f\"centroid_{idx}.npy\")\n",
        "\n",
        "#     centroid = np.load(origin_centroid_path).astype(np.float32)\n",
        "#     centroid_h5 = data[\"cell|centroid\"][:].astype(np.float32)\n",
        "\n",
        "#     pressure_h5 = data[\"cell|pressure\"][:].astype(np.float32)\n",
        "#     pressure_gt = np.load(os.path.join(origin_B_data, f\"press_{idx}.npy\")).astype(\n",
        "#         np.float32\n",
        "#     )[:, None]\n",
        "\n",
        "#     origin_mesh_path = os.path.join(origin_B_mesh, f\"mesh_{idx}.ply\")\n",
        "#     car_model = trimesh.load(origin_mesh_path, force=\"mesh\")\n",
        "#     pos = car_model.vertices.astype(np.float32)\n",
        "#     pos_h5 = data[\"node|pos\"][:].astype(np.float32)\n",
        "\n",
        "#     centroid_eq = np.allclose(centroid_h5, centroid, rtol=1e-05, atol=1e-08)\n",
        "#     label_eq = np.allclose(pressure_h5, pressure_gt, rtol=1e-05, atol=1e-08)\n",
        "#     centroid_eq = np.allclose(centroid_h5, centroid, rtol=1e-05, atol=1e-08)\n",
        "#     pos_eq = np.allclose(pos_h5, pos, rtol=1e-05, atol=1e-08)\n",
        "\n",
        "#     if not centroid_eq or not label_eq:\n",
        "#         print(f\"data {idx} is not eq!\")\n",
        "#         return False\n",
        "#     return True\n",
        "\n",
        "\n",
        "# flag = True\n",
        "# with h5py.File(train_h5) as h5_handle:\n",
        "#     samples_indices = list(h5_handle.keys())\n",
        "#     n = len(samples_indices)\n",
        "#     for i, idx in enumerate(samples_indices):\n",
        "#         data = h5_handle[idx]\n",
        "#         if i < n // 2:\n",
        "#             flag = compare_A_data(data, idx)\n",
        "#         else:\n",
        "#             flag = compare_B_data(data, idx)\n",
        "#         if not flag:\n",
        "#             break\n",
        "\n",
        "\n",
        "# if not flag:\n",
        "#     print(\"与原始数据不一致!!!!!!!!!\")\n",
        "# else:\n",
        "#     print(\"数据一致  !!!!!!!!!!!!!!\")\n",
        "\n"
      ],
      "metadata": {
        "id": "YVUwRDRG_STj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce87a0e7-5121-41a1-ea7c-9b4b530aafd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "样本总数: 1000, 前50%是trackA的数据集, 后50%是trackB的数据集\n",
            ">>>>>>>>>>>>>>>>>  Track A数据的样本属性和解释  >>>>>>>>>>>>>>>>>\n",
            "属性                  \t形状                       \t解释                                                \t数据类型\n",
            "PN_mean_std         \t(2, 6)                   \t坐标和法向的mean与std                                    \tfloat32\n",
            "bounds              \t(2, 3)                   \t该数据的坐标bound                                       \tfloat64\n",
            "cells_face          \t(21504, 1)               \t构成单元的边索引                                          \tint64\n",
            "cells_index         \t(21504, 1)               \t单元的索引, 与cells_face对应                              \tint64\n",
            "cells_node          \t(21504, 1)               \t构成cell的点索引                                        \tint64\n",
            "cell|cells_area     \t(7168, 1)                \tcell的面积                                           \tfloat32\n",
            "cell|centroid       \t(7168, 3)                \t单元的中心坐标                                           \tfloat32\n",
            "face|face_center_pos\t(10752, 3)               \t单元中心点的邻接关系                                        \tfloat32\n",
            "face|face_length    \t(10752, 1)               \t边的长度                                              \tfloat32\n",
            "face|face_node      \t(2, 10752)               \t构成边的点索引                                           \tint64\n",
            "face|neighbour_cell \t(2, 10752)               \t与边邻接的cell索引                                       \tint64\n",
            "node|ao             \t(3586, 1)                \t预计算属性, ambient occlusion                          \tfloat64\n",
            "node|pos            \t(3586, 3)                \t坐标                                                \tfloat32\n",
            "node|pressure       \t(3586, 1)                \t压强                                                \tfloat64\n",
            "node|unit_norm_v    \t(3586, 3)                \t单位法向                                              \tfloat32\n",
            "pressure_mean_std   \t(2,)                     \t该数据样本压强的mean和std, trackA和trackB分别是各自的一组           \tfloat32\n",
            "voxel_mean_std      \t(2,)                     \tsdf的mean和std                                      \tfloat32\n",
            "voxel|grid          \t(64, 64, 64, 3)          \tSDF场的查询点坐标                                        \tfloat64\n",
            "voxel|sdf           \t(262144,)                \tSDF场                                              \tfloat64\n",
            "\n",
            ">>>>>>>>>>>>>>>>>  Track B数据的样本属性和解释  >>>>>>>>>>>>>>>>>\n",
            "属性                  \t形状                       \t解释                                                \t数据类型\n",
            "PN_mean_std         \t(2, 6)                   \t坐标和法向的mean与std                                    \tfloat32\n",
            "bounds              \t(2, 3)                   \t该数据的坐标bound                                       \tfloat64\n",
            "cells_index         \t(2510718, 1)             \t单元的索引, 与cells_face对应                              \tint64\n",
            "cells_node          \t(2510718, 1)             \t构成cell的点索引                                        \tint64\n",
            "cell|ao             \t(384258, 1)              \t预计算属性, ambient occlusion                          \tfloat32\n",
            "cell|area           \t(384258, 1)              \t单元的面积                                             \tfloat64\n",
            "cell|centroid       \t(384258, 3)              \t单元的中心坐标                                           \tfloat64\n",
            "cell|pressure       \t(384258, 1)              \t在单元上的压强                                           \tfloat64\n",
            "cell|unit_norm_v    \t(384258, 3)              \tcell的法向                                           \tfloat32\n",
            "face|neighbour_cell \t(2, 812098)              \t与边邻接的cell索引                                       \tint64\n",
            "node|pos            \t(418461, 3)              \t坐标                                                \tfloat64\n",
            "node|unit_norm_v    \t(418461, 3)              \t单位法向                                              \tfloat64\n",
            "pressure_mean_std   \t(2,)                     \t该数据样本压强的mean和std, trackA和trackB分别是各自的一组           \tfloat32\n",
            "voxel_mean_std      \t(2,)                     \tsdf的mean和std                                      \tfloat32\n",
            "voxel|grid          \t(64, 64, 64, 3)          \tSDF场的查询点坐标                                        \tfloat64\n",
            "voxel|sdf           \t(262144,)                \tSDF场                                              \tfloat32\n",
            "数据一致  !!!!!!!!!!!!!!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O2Lvi6K2HIlK"
      },
      "source": [
        "# Start Training\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PAVOoHWAHIlK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a5a6b8c-d329-4914-9a94-55d46991c61d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "seed:1720698571\n",
            "Training traj has been loaded time consuming:0.006746530532836914\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/0.state\n",
            "Epoch 0 train loss: 1.0670708417892456\n",
            "Epoch 0 eval loss: 0\n",
            "Epoch 0 completed in 7.39 seconds\n",
            "Epoch 1 train loss: 2.411750912666321\n",
            "Epoch 1 eval loss: 0\n",
            "Epoch 1 completed in 2.81 seconds\n",
            "Epoch 2 train loss: 1.1559545993804932\n",
            "Epoch 2 eval loss: 0\n",
            "Epoch 2 completed in 2.79 seconds\n",
            "Epoch 3 train loss: 1.0345410108566284\n",
            "Epoch 3 eval loss: 0\n",
            "Epoch 3 completed in 2.79 seconds\n",
            "Epoch 4 train loss: 1.0356269478797913\n",
            "Epoch 4 eval loss: 0\n",
            "Epoch 4 completed in 2.80 seconds\n",
            "Epoch 5 train loss: 1.014283537864685\n",
            "Epoch 5 eval loss: 0\n",
            "Epoch 5 completed in 2.83 seconds\n",
            "Epoch 6 train loss: 1.0050128400325775\n",
            "Epoch 6 eval loss: 0\n",
            "Epoch 6 completed in 2.84 seconds\n",
            "Epoch 7 train loss: 1.0144437551498413\n",
            "Epoch 7 eval loss: 0\n",
            "Epoch 7 completed in 2.80 seconds\n",
            "Epoch 8 train loss: 0.9937323331832886\n",
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            "Epoch 9 train loss: 0.9978521168231964\n",
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            "Epoch 10 train loss: 0.9917979836463928\n",
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            "Epoch 10 completed in 2.82 seconds\n",
            "Epoch 11 train loss: 0.984153151512146\n",
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            "Epoch 12 train loss: 0.980357438325882\n",
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            "Epoch 13 train loss: 0.970615953207016\n",
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            "Epoch 14 train loss: 0.9595661163330078\n",
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            "Epoch 17 train loss: 0.9195977747440338\n",
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            "Epoch 18 train loss: 0.8930555880069733\n",
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            "Epoch 19 train loss: 0.8646275401115417\n",
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            "Epoch 20 train loss: 0.8521712422370911\n",
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            "Epoch 21 train loss: 0.8769820332527161\n",
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            "Epoch 22 train loss: 0.8091692328453064\n",
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            "Epoch 71 train loss: 0.4990030378103256\n",
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            "Epoch 72 train loss: 0.46048858761787415\n",
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            "Epoch 73 completed in 2.88 seconds\n",
            "Epoch 74 train loss: 0.460318461060524\n",
            "Epoch 74 eval loss: 0\n",
            "Epoch 74 completed in 2.90 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/75.state\n",
            "Epoch 75 train loss: 0.43338727951049805\n",
            "Epoch 75 eval loss: 0\n",
            "Epoch 75 completed in 4.28 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/76.state\n",
            "Epoch 76 train loss: 0.45667001605033875\n",
            "Epoch 76 eval loss: 0\n",
            "Epoch 76 completed in 4.29 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/77.state\n",
            "Epoch 77 train loss: 0.478761225938797\n",
            "Epoch 77 eval loss: 0\n",
            "Epoch 77 completed in 4.30 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/78.state\n",
            "Epoch 78 train loss: 0.43545985221862793\n",
            "Epoch 78 eval loss: 0\n",
            "Epoch 78 completed in 4.34 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/79.state\n",
            "Epoch 79 train loss: 0.4459283798933029\n",
            "Epoch 79 eval loss: 0\n",
            "Epoch 79 completed in 4.34 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/80.state\n",
            "Epoch 80 train loss: 0.40236082673072815\n",
            "Epoch 80 eval loss: 0\n",
            "Epoch 80 completed in 4.35 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/81.state\n",
            "Epoch 81 train loss: 0.4359124004840851\n",
            "Epoch 81 eval loss: 0\n",
            "Epoch 81 completed in 4.32 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/82.state\n",
            "Epoch 82 train loss: 0.3815916180610657\n",
            "Epoch 82 eval loss: 0\n",
            "Epoch 82 completed in 4.37 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/83.state\n",
            "Epoch 83 train loss: 0.47184717655181885\n",
            "Epoch 83 eval loss: 0\n",
            "Epoch 83 completed in 4.29 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/84.state\n",
            "Epoch 84 train loss: 0.383882611989975\n",
            "Epoch 84 eval loss: 0\n",
            "Epoch 84 completed in 4.32 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/85.state\n",
            "Epoch 85 train loss: 0.40830063819885254\n",
            "Epoch 85 eval loss: 0\n",
            "Epoch 85 completed in 4.34 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/86.state\n",
            "Epoch 86 train loss: 0.4083956331014633\n",
            "Epoch 86 eval loss: 0\n",
            "Epoch 86 completed in 4.32 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/87.state\n",
            "Epoch 87 train loss: 0.41460123658180237\n",
            "Epoch 87 eval loss: 0\n",
            "Epoch 87 completed in 4.29 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/88.state\n",
            "Epoch 88 train loss: 0.45953524112701416\n",
            "Epoch 88 eval loss: 0\n",
            "Epoch 88 completed in 4.29 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/89.state\n",
            "Epoch 89 train loss: 0.4072156548500061\n",
            "Epoch 89 eval loss: 0\n",
            "Epoch 89 completed in 4.33 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/90.state\n",
            "Epoch 90 train loss: 0.44486764073371887\n",
            "Epoch 90 eval loss: 0\n",
            "Epoch 90 completed in 4.31 seconds\n",
            "Simulator model saved at /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/91.state\n",
            "Epoch 91 train loss: 0.41741743683815\n",
            "Epoch 91 eval loss: 0\n",
            "Epoch 91 completed in 4.31 seconds\n",
            "Early stopping\n",
            "Training completed\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "import os\n",
        "import torch\n",
        "from torch.optim import Adam\n",
        "from dataset.Load_mesh import DatasetFactory\n",
        "from utils import get_param, scheduler\n",
        "import time\n",
        "from utils.get_param import get_hyperparam\n",
        "from utils.Logger import Logger\n",
        "from utils.losses import LpLoss\n",
        "import random\n",
        "import datetime\n",
        "from torch_geometric.data import Batch\n",
        "\n",
        "# configurate parameters\n",
        "params, git_info = get_param.params()\n",
        "\n",
        "# git information\n",
        "if git_info is not False:\n",
        "    git_info = {\n",
        "        \"git_branch\": params.git_branch,\n",
        "        \"git_commit_dates\": params.git_commit_dates,\n",
        "    }\n",
        "else:\n",
        "    git_info = {\"git_branch\": \" \", \"git_commit_dates\": \" \"}\n",
        "\n",
        "# for saving model\n",
        "# torch.manual_seed(0)\n",
        "# torch.set_num_threads(4)\n",
        "# seed = int(datetime.datetime.now().timestamp())\n",
        "seed = 1720698571\n",
        "random.seed(seed)\n",
        "torch.manual_seed(seed)\n",
        "print(f'seed:{seed}')\n",
        "# random.seed(seed)\n",
        "# torch.manual_seed(seed)\n",
        "torch.cuda.set_per_process_memory_fraction(0.99, params.on_gpu)\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# initialize Logger and load model / optimizer if according parameters were given\n",
        "logger = Logger(\n",
        "    get_hyperparam(params),\n",
        "    use_csv=True,\n",
        "    use_tensorboard=False,\n",
        "    params=params,\n",
        "    git_info=git_info,\n",
        "    copy_code=False,\n",
        ")\n",
        "\n",
        "# initialize Training Dataset\n",
        "start = time.time()\n",
        "datasets_factory = DatasetFactory(\n",
        "    params=params,\n",
        "    device=device,\n",
        "    split='train',\n",
        ")\n",
        "# train_indices = list(range(0, 480)) + list(range(500, 980))\n",
        "# val_indices = list(range(480, 500)) + list(range(980, 1000))\n",
        "\n",
        "train_indices = list(range(0, 500)) + list(range(500, 1000))\n",
        "val_indices = list(range(499, 500)) + list(range(999, 1000))\n",
        "\n",
        "# params.batch_size = 1\n",
        "# create dataset objetc\n",
        "train_dataset, train_loader = datasets_factory.create_trainset(\n",
        "    batch_size=params.batch_size,\n",
        "    num_workers=0,\n",
        "    pin_memory=False,\n",
        "    persistent_workers=False,\n",
        "    indices=train_indices,\n",
        "    subsampling=True,  # 使用NeighborSampler\n",
        "    ratio=0.1,\n",
        ")\n",
        "valid_dataset, valid_loader = datasets_factory.create_trainset(\n",
        "    batch_size=1,\n",
        "    num_workers=0,\n",
        "    pin_memory=False,\n",
        "    persistent_workers=False,\n",
        "    indices=val_indices,\n",
        "    subsampling=False,  # 不使用NeighborSampler\n",
        ")\n",
        "end = time.time()\n",
        "print(\"Training traj has been loaded time consuming:{0}\".format(end - start))\n",
        "\n",
        "# initialize fluid model\n",
        "from NN.Transolver.SageTrans_importer_B import FVGN\n",
        "\n",
        "model = FVGN(params)\n",
        "\n",
        "fluid_model = model.to(device)\n",
        "fluid_model.train()\n",
        "optimizer = Adam(fluid_model.parameters(), lr=params.lr)\n",
        "\n",
        "\"\"\" >>> lr scheduler settings >>> \"\"\"\n",
        "before_explr_decay_steps = int(params.n_epochs * 0.7)\n",
        "two_step_scheduler = scheduler.ExpLR(\n",
        "    optimizer, decay_steps=params.n_epochs - before_explr_decay_steps, gamma=1e-4\n",
        ")\n",
        "lr_scheduler = scheduler.GradualStepExplrScheduler(\n",
        "    optimizer,\n",
        "    multiplier=1.0,\n",
        "    milestone=[int(params.n_epochs / 2)],\n",
        "    gamma=0.1,\n",
        "    total_epoch=before_explr_decay_steps,\n",
        "    after_scheduler=two_step_scheduler,\n",
        "    expgamma=1e-2,\n",
        "    decay_steps=params.n_epochs - before_explr_decay_steps,\n",
        "    min_lr=1e-6,\n",
        ")\n",
        "\"\"\" <<< lr scheduler settings <<< \"\"\"\n",
        "\n",
        "if (\n",
        "    params.load_latest\n",
        "    or params.load_date_time is not None\n",
        "    or params.load_index is not None\n",
        "):\n",
        "    logger.load_logger(datetime=params.load_date_time)\n",
        "    # load_logger = Logger(get_hyperparam(params),use_csv=False,use_tensorboard=params.log,params=params,git_info=git_info)\n",
        "    if params.load_optimizer:\n",
        "        params.load_date_time, params.load_index = logger.load_state(\n",
        "            model=fluid_model,\n",
        "            optimizer=optimizer,\n",
        "            scheduler=lr_scheduler,\n",
        "            datetime=params.load_date_time,\n",
        "            index=params.load_index,\n",
        "            device=device,\n",
        "        )\n",
        "    else:\n",
        "        params.load_date_time, params.load_index = logger.load_state(\n",
        "            model=fluid_model,\n",
        "            optimizer=None,\n",
        "            scheduler=None,\n",
        "            datetime=params.load_date_time,\n",
        "            index=params.load_index,\n",
        "            device=device,\n",
        "        )\n",
        "    params.load_index = int(params.load_index)\n",
        "    print(f\"loaded: {params.load_date_time}, {params.load_index}\")\n",
        "\n",
        "params.load_index = 0 if params.load_index is None else params.load_index\n",
        "\n",
        "lp_loss = LpLoss(size_average=True)\n",
        "\n",
        "# 初始化用于收集整个数据集的残差的张量\n",
        "epoc_loss = 0\n",
        "epoc_val_loss = 0\n",
        "epoch_val_loss_list = [999]\n",
        "\n",
        "# training loop\n",
        "for epoch in range(params.n_epochs):\n",
        "\n",
        "    fluid_model.train()\n",
        "    epoc_loss = 0\n",
        "\n",
        "    start = time.time()\n",
        "\n",
        "    for batch_index, graph_cell in enumerate(train_loader):\n",
        "\n",
        "        optimizer.zero_grad()\n",
        "\n",
        "        graph_cell = train_dataset.datapreprocessing(\n",
        "            graph_cell.cuda(), is_training=True\n",
        "        )\n",
        "\n",
        "        pred_node = fluid_model(\n",
        "            graph_cell=graph_cell,\n",
        "            params=params,\n",
        "            is_training=True,\n",
        "        )\n",
        "\n",
        "        loss = lp_loss(\n",
        "            pred_node,\n",
        "            graph_cell.norm_y,\n",
        "            batch=graph_cell.batch,\n",
        "            num_graphs=graph_cell.num_graphs,\n",
        "        )\n",
        "        loss.backward()\n",
        "        optimizer.step()\n",
        "        epoc_loss += loss.cpu().item()\n",
        "\n",
        "    epoc_loss = epoc_loss / len(train_loader)\n",
        "    if epoch == 0:\n",
        "      model_saving_path = logger.save_state(\n",
        "          model=fluid_model,\n",
        "          optimizer=optimizer,\n",
        "          scheduler=lr_scheduler,\n",
        "          index=epoch,\n",
        "      )\n",
        "    if epoch >= 75:\n",
        "      model_saving_path = logger.save_state(\n",
        "          model=fluid_model,\n",
        "          optimizer=optimizer,\n",
        "          scheduler=lr_scheduler,\n",
        "          index=epoch,\n",
        "      )\n",
        "\n",
        "    print(f\"Epoch {epoch} train loss: {epoc_loss}\")\n",
        "    print(f\"Epoch {epoch} eval loss: {epoc_val_loss}\")\n",
        "    print(f\"Epoch {epoch} completed in {time.time() - start:.2f} seconds\")\n",
        "    logger.add_log_item(\"Epcoh_loss\", epoc_loss, epoch)\n",
        "    logger.log(f\"Epoch_eval_loss\", epoc_val_loss, epoch)\n",
        "\n",
        "    lr_scheduler.step()\n",
        "\n",
        "    if epoch > 90:\n",
        "        print(\"Early stopping\")\n",
        "        break\n",
        "\n",
        "print(\"Training completed\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBCalHN7bBji"
      },
      "source": [
        "# 以C榜为例的输出规范\n",
        "(将答案文件夹压缩为B_result.zip,用于被程序识别，假设文件路径是Example_C)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bw1fM4KwHIlK",
        "outputId": "849a7d75-108b-43fa-89c8-50c106076dd6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Simulator model and optimizer/scheduler loaded checkpoint Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/states/80.state\n",
            "loaded: 2024-07-16-18:40:34, 80\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_38.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_17.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_27.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_20.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_41.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_30.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_39.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_4.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_6.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_15.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_34.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_12.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_23.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_8.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_47.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_48.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_42.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_22.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_37.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_3.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_44.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_46.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_14.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_19.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_21.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_18.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_31.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_36.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_26.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_16.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_43.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_10.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_32.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_28.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_40.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_1.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_9.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_11.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_7.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_33.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_29.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_24.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_50.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_49.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_13.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_5.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_45.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_2.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_35.npy\n",
            "npy file saved:/content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C/press_25.npy\n",
            "Generating answer completed completed in 47.35 seconds\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "import os\n",
        "import torch\n",
        "from dataset.Load_mesh import DatasetFactory\n",
        "import time\n",
        "from utils.losses import LpLoss\n",
        "\n",
        "# # configurate parameters\n",
        "params.load_index = \"80\"\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# initialize Training Dataset\n",
        "datasets_factory = DatasetFactory(\n",
        "    params=params,\n",
        "    device=device,\n",
        ")\n",
        "\n",
        "test_indices = list(range(50, 100))\n",
        "# create dataset objetc\n",
        "test_dataset, test_loader = datasets_factory.create_testset(\n",
        "    batch_size=1,\n",
        "    num_workers=0,\n",
        "    pin_memory=False,\n",
        "    persistent_workers=False,\n",
        "    valid_num=len(test_indices),\n",
        "    subsampling=False,\n",
        "    indices=test_indices\n",
        ")\n",
        "\n",
        "# initialize fluid model\n",
        "from NN.Transolver.SageTrans_importer_B import FVGN\n",
        "\n",
        "model = FVGN(params)\n",
        "\n",
        "fluid_model = model.to(device)\n",
        "fluid_model.eval()\n",
        "\n",
        "params.load_date_time, params.load_index = logger.load_state(\n",
        "    model=fluid_model,\n",
        "    optimizer=None,\n",
        "    scheduler=None,\n",
        "    datetime=logger.datetime,\n",
        "    index=params.load_index,\n",
        "    device=device,\n",
        ")\n",
        "params.load_index = params.load_index\n",
        "print(f\"loaded: {params.load_date_time}, {params.load_index}\")\n",
        "\n",
        "params.load_index = 0 if params.load_index is None else params.load_index\n",
        "\n",
        "start = time.time()\n",
        "with torch.no_grad():\n",
        "\n",
        "    epoc_val_loss = 0\n",
        "\n",
        "    for batch_index, graph_cell in enumerate(test_loader):\n",
        "\n",
        "        graph_cell = test_dataset.datapreprocessing(\n",
        "            graph_cell.cuda(),\n",
        "            is_training=False,\n",
        "        )\n",
        "\n",
        "        pred_node_valid = fluid_model(\n",
        "            graph_cell=graph_cell,\n",
        "            is_training=False,\n",
        "            params=params,\n",
        "        )\n",
        "\n",
        "        reversed_node_press = (\n",
        "            (pred_node_valid - 1e-8) * graph_cell.press_std\n",
        "        ) + graph_cell.press_mean\n",
        "\n",
        "        logger.save_test_results(\n",
        "            value=reversed_node_press.cpu().detach().squeeze().numpy(),\n",
        "            num_id=\"\".join(\n",
        "                [chr(ascii_code) for ascii_code in graph_cell.origin_id.cpu().tolist()]\n",
        "            )[2:],\n",
        "        )\n",
        "\n",
        "print(f\"Generating answer completed completed in {time.time() - start:.2f} seconds\")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_R4ymI9BcKYb",
        "outputId": "271a7823-c6fb-4125-98a0-6fb0bd6ec481"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Files from /content/Logger/net SAGE-Trans; hs 128;/2024-07-16-18:40:34/gen_answers_C have been compressed into /content/B_Results.zip\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import zipfile\n",
        "\n",
        "# 获取要压缩的目录路径\n",
        "dir_to_zip = f\"{logger.saving_path}/gen_answers_C\"\n",
        "# 设置压缩包的名称和路径\n",
        "zip_filename = \"B_Results.zip\"\n",
        "current_directory = os.getcwd()\n",
        "zip_filepath = os.path.join(current_directory, zip_filename)\n",
        "\n",
        "# 创建压缩包\n",
        "with zipfile.ZipFile(zip_filepath, 'w', zipfile.ZIP_DEFLATED) as zipf:\n",
        "    for root, dirs, files in os.walk(dir_to_zip):\n",
        "        for file in files:\n",
        "            # 创建文件的完整路径\n",
        "            file_path = os.path.join(root, file)\n",
        "            # 将文件写入压缩包，并保持文件在压缩包中的相对路径\n",
        "            zipf.write(file_path, os.path.relpath(file_path, dir_to_zip))\n",
        "\n",
        "print(f\"Files from {dir_to_zip} have been compressed into {zip_filepath}\")\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "gpuType": "L4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.14"
    },
    "accelerator": "GPU"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}